brand valuation using royalty relief method and...

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BRAND VALUATION USING ROYALTY RELIEF

METHOD AND LINEAR DISCRIMINANT ANALYSIS.

Gracia Rubio Martín. Francisco Pérez Hernández. Conrado M. Manuel García.

U. Complutense de Madrid. U. Autónoma de Madrid. U. Complutense de Madrid. [email protected] [email protected] [email protected]

TEL.+34 667821171. TEL.+34 692138666. TEL.+34 669288299.

ABSTRACT.

The aim of this paper is to introduce a statistical procedure to value a brand, by

means of which firms may be able to determine the level of implicit royalty that

they would charge for the use of their brand, applying multivariate techniques

from market references. In this manner, the study has been based on a

statistical contrast of the royalties paid in Spanish franchises belonging to three

different industries: food, health and beauty and fashion. Each industry has

been segmented using cluster techniques, and then, through linear

discriminating analysis, a model is proposed to explain the royalty paid

according to certain economic-financial figures of the companies. The implicit

impartiality in the development of the model means that it could be generally

accepted by analysts, consultants and companies who need to determine the

value of a brand.

Key words: brand valuation, intangible, value driver, fair value, discriminating analysis.

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1. INTRODUCTION: AN APPROACH TO THE PROBLEM OF INTANGIBLE

ASSETS.

In the last three decades some authors, such as Lev (1989, 2000,

2005), have shown the gradual increase between firms´ market value and their

book value. Other authors, such as Brown et al (1999), or Dantoh et al (2004)

also warn about how equity and profits have lost relevance for explaining the

market value of American companies. In the Spanish case we can observe the

work of Iñiguez and López (2006), who introduce a measure of the intangible

value not explained by financial economic magnitudes of the firms, but paid in

the prices of business transactions (around 50%). Also interesting is the work of

Rubio et al (2013), who find that for the pharmaceutical and biotechnology

sectors, heavily associated with research and development, these quantities

paid and unexplained are higher than 69%. Others empirical results, like Han-

Min Wang et al (2012), show that the brand equity of companies is mainly

affected by their advertising expenditure and brand equity has a significant

positive impact on market value in the Taiwan’s financial service companies. All

these works suggest a change in the business model: this has moved from a

system based on industrial production to another one in which firms base their

success on knowledge. Drucker (1993) points out that this transformation is due

to a reorganization process in society and its way of looking at the world and its

view about basic values.

However, intellectual capital, defined as the research, development of

new products, strategy, brand and corporate image has turned into a powerful

generator of firms’ value. For that reason, academics as well as accounting and

financial professionals have tried to establish models with which they can

measure the value of the different kinds of intangible assets. In mergers and

acquisitions, and also in business combinations, the necessity of intangible

valuation becomes more urgent. In these cases, International Accounting

Standards, and in particular the International Financial Reporting Standard 3,

also known as IFRS 3, state that the purchasing company will have the

obligation to identify and measure each one of the assets and liabilities derived

from the operation performed, including even tangible and intangible assets.

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Such assets as the International Accounting Standards 38 (IAS 38) and

the aforementioned IFRS 3 establish will be quantified by their fair value, which

is defined as the amount for which each asset can be exchanged, or a liability

cancelled, between the concerned parties, properly informed. The transaction

must be performed on mutual independence terms, including market

expectation and, at the same time, excluding the synergies for a particular

buyer. Besides, both standards impose a hierarchy in the valuation criteria (see

Figure 1): market and income methodology. If none of these could be used,

then the replacement cost methodology would be applied.

Figure 1: Synthesis of intangible evaluating criterion.

Source: own elaboration.

Specifically, market methodologies involve the use of multiples, which

will depend on prices paid in previous transactions. Nevertheless, one problem

of this method is the absence of a database, by business areas, about prices

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paid for individual intangible assets which have similar characteristics to these

that need to be valued.

Likewise, income methodologies have had greater academic and

professional growth. A royalty-saving method is one of the most common

typologies, specially used to assess brands and patents, and it is the one that

has been introduced in different ways by consultant agencies such as Brand

finance (2000), Intangible Business (2001, 2004), Whitwell (2003), and AUS

Consultants or Consor. These last two consultant agencies were included in

Salinas (2007). This method is based on the measurement of the royalty after

the payment of taxes or license payments that have been saved as a

consequence of having the ownership of the asset. It requires market data of

licenses and royalties applied to patent valuation. The problem with these

methods, which take market-related data, is that they lack the mathematical

techniques and contrasts them to provide reliability. They do not test if the

companies used as a reference are appropriate, or whether the variables and

multiples used provide suitable and sufficient information on royalties paid, like

we show later.

Other income methods frequently employed for intangible assessment

are: the multiple periods earning excess of return, incremental cash flow and

real options. The first one is built on operating cash flows of the enterprise and,

from these will be deducted in order to determine the cash flow arising from the

excess of return, the charges of the other contributory assets (tangibles and

intangibles). Authors such as Smith (1997) or Lev (2001) and consulting

agencies like Brand Economics (2002) base their proposal on this technique.

Concerning the incremental cash flows method, its main goal is the

measurement of the increase of these cash flows due to a price premium in

goods and services sold by companies or savings that come from the

ownership of the intangible asset. Some academics, such as Damodaran

(1997), Reilly (1999) or Fernández (2007) have developed this methodology.

Consulting agencies such as Interbrand1 or BBDO (2001, 2002), through the

division Brand Equity, also have used it in many modalities. Finally, the real

options method includes uncertainty and risk as variables in the valuation of the

1 Technique exposed in Motameni (1998) and in Ratnatunga and Ewing (2008).

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assets depending on companies’ expectations. Despite the fact that this method

is not too frequently used by consulting agents, AICPA (2011) highlights that

this methodology is one of the best available to compute the valuation of some

assets like patents or research and development expenditures for innovative

and technology-based company or even the generation of brands in competitive

environments, because one of the main features of this method is the flexibility

that it incorporates. Among the authors who have proposed this technique in

diverse variations Myers (1977), Kulatilaka (2000), Schwartz (2001) and Rubio

and Lamothe (2010) are worthy of mention.

Nevertheless, despite the efforts that have been made, there is still a

large heterogeneity present among all the methodologies shown above. Salinas

(2007) collects 46 different methods of trademark assessment and, as we are

going to explain in detail, some of them include high doses of subjectivism in

the calculation of their magnitude. For that reason, the development of valuation

proposals, in an objective and reliable way, for intangible assets, is still a

challenge for economic science.

The goal of this paper is to introduce a statistical procedure which could

be generally accepted, by means of which firms may be able to determine the

level of an implicit royalty that they would charge for the use of their brand,

applying multivariate techniques from market references.

This paper is organized as follows: after the Introduction, in the second

section the problems involved in the calculation of the royalty relief are

analyzed. In section 3 the data, hypothesis, theoretical framework and basis of

the proposed model are developed. In section 4 we deal with the applications

and results of the model from the available sample and finally, the article ends

with the conclusions and future lines of research.

2. THE PROBLEMS INVOLVED IN THE CALCULATION OF THE

ROYALTY RELIEF.

Brands are, indisputably, one of the most important sources of value of

companies. But the great problem when valuing them, as pointed out by

Fernández (2004), is to properly delimit the corresponding flow, from the

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objective data and also this data to capture what any independent investor of

the market would be willing to pay for it.

Brands can be defined as distinctive signs of products or services on the

market which are usually protected for their owner by registration rights. So the

benefits that can be attributed are the ones which derive directly from its use,

through any of the methods pointed out above. Nevertheless, brands also

include psychological aspects, in other words, symbolic constructions in the

users’ minds on the basis of which future expectations about their functioning

will be generated. In this sense, Aaker suggests a brand measuring model

called The Brand Equity Ten, based on aspects such as loyalty, quality and

leadership, differentiation from other products, the awareness the Brand

awakens in the mind of the consumer and the behavior of the market. Many

times the so called brand strength is used to summarize all these aspects, but

the methodologies occasionally developed by consultants, such as BBO,

Interbrand, Brand Economics, Financial Word or Intangible Business, among

others, do not have a mathematical and statistical basis to justify their use or its

quantifying. And thus, both their treatment and the modeling of the intangible

asset have large amounts of subjectivity.

In order to illustrate all of this, the pricing model of the English consultant

Interbrand was taken as an example. As explained in Ratnatunga and Michael

(2008) or in Salinas (2007) this proposal mixes financial variables and others

coming from marketing. Firstly it calculates the brand's relative profit, deducting

that coming from a generic asset or a white trade mark, using the industry

ratios2: tangible assets over sales and tangible assets over the profit. This result

(the portion of profit corresponding to the intangible assets) is multiplied by the

“brand strength factor”, a key factor which was previously calculated through

qualitative variables: (i) leadership or a brand's ability to hold a dominant market

share, which receives a maximum score of 25 points, (ii) stability or ability to

retain loyalty for long time periods (15 points) (iii) market, taking into account

that certain markets involve greater abilities to grow than others (10 points), (iv) 2 The multiple is a ratio that incorporates in its numerator the value of the company's economic structure, defined as the market

price plus the net financial debt, and in its denominator an economic variable such as the sales, for example. If the ratio in a certain

sector is 2, this means that, on average, the economic business structure is valued at twice the sales. If the numerator is replaced for

the tangible asset value, and this multiple is 0.7, for example, then the difference between both multiples: 2-0.7=1.30 is the part that

can be attributed to the intangible asset. Thus 65% of the sales flow would correspond to the intangible structure.

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support given by the organization itself for an appropriate management (25

points), (v) international image, if it has an international image it would receive a

maximum of 10 points, and, lastly, (vi) tendency or ability to maintain its

relevance (5 points). At the same time, the brand's strength factor is functionally

related to the PER ratio (Price-Earnings Ratio or number of times the price

exceeds the profit) through an S-shaped curve. Interbrand indicates that this

curve comes from a comparison carried out by the company about a study of

comparable brands. In line with the conclusions by Fernández (2001), Coronas

(2002) or Tollington (1999), there is a great subjectivity in the calculation of

brand strength through the method explained before when determining the

factors and when weighting these. This is due to the lack of a statistical contrast

that justifies the proposed use. Furthermore, the way by which the relation

between the PER ratio and the strength factor is introduced via a mathematical

function, does not include the inherent variability which is present in the real

world.

Something similar applies for proposals based on the royalty technique,

on which the development of this article is based. First, brand valuation, as

detailed in the following image (figure2), consists of a long run estimation of the

companies' sales.

Figure 2. Royalty according to Brand Finance

Source: Salinas (2007)

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Second, it is necessary to determine the royalty rate which would

correspond to that sales forecast. Then this rate would be multiplied by the

forecast sales and so we find the cash flow corresponding to the brand. Third, it

is necessary to determine a proper discount rate which must include the risk

associated with the valued asset and, lastly, it would suffice to update the cash

flows and find the sum of the updated assets whose result would correspond to

the brand's value.

Although the method is simple, when a company has a product but it

does not collect any money for the license, the calculation of the proper royalty,

in the event of an assignment of the brand becomes more difficult and, as in the

previous case, the proposed models do not include the impartiality and

accuracy required by the process. For instance, according to Salinas (2007),

Consor consulting is developing a model based on the licenses of other similar

brands. To do this, they start by valuing the brand strength according to twenty

determining factors, among which the following are included: profit margins,

brand's life cycle state, transferability and its international protection. Regarding

the comparison's purposes, the company uses a database of 10.000 licensing

operations from which is obtained the royalty rate that will be applied to the sale

price of the company. Then, we just mention the application of multipliers such

as the brand's remaining life, the company's sales growth and the discount rate.

For its part, the proposal of Intangible Business Ltd (2001, 2004), also

determines the brand strength against competitors through the variables that

are shown in Figure 3. Thus, this figure also contains measures related with the

strength of the different trademarks that participate in the market.

Figure 3. Analysis of the brand's relative strength: Benchmark.

Source: Intangible Business (2004)

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The reference competitor, Benchmark, collects an explicit royalty, the

market's maximum, and from this the score of the valued brand is obtained. If,

as in figure 3, the score obtained by the valued company is 58 and the market's

maximum is 76, with a royalty of 5%, a rule of three, would suffice to find that

the royalty that would correspond to a score of 58 is 3.82%.

As shown in the figure 3, the variables that nurture the brand strength are

not the same in the three models (Interbrand, Consor Consulting and Intangible

Business), given the non-existence of generally accepted criteria, which leads

to the inclusion of great doses of discretion. The same happens when

determining their weights, with both lacking an aforementioned mathematical

and statistical contrast. The factors or variables often redundant or even

correlated with each other, thereby, the whole process leads to the calculation

of a royalty which is probably biased, but, in addition, its calculation is

deterministic, as this is also the case for Interbrand, and it does not take into

account the random nature of the market's in which it is involved. Finally, the

methods assume that the factors and their weights are the same for all

industrial sectors, regions or categories, when in fact they may vary.

Nevertheless, statistical techniques have been used in other brand

analyses, but only in works that deal with the utility that it generates in

consumers and in their decisions as a direct result of the variables or attributes

owned by a brand or product. For instance, Punj et al (1983) explained issues

and problems related to the use and validation of cluster analysis to discover a

preliminary identification of clusters in marketing research. Perreault et al (1979)

show, across discriminant analysis, a better understanding of how some groups

of customers or items differ as a consequence of some set of explanatory

metric variables, such as a set of attributes or performance ratings. Sivakumar

et al (1997) have proved that higher quality brands are generally less affected

by a rise in prices than lower quality brands. Other remarkable works are the

ones about the application of joint analysis3, like Kamakura and Russell (1993),

Swait et al (1993), or the ones about logit regression techniques, as in Green

3 It is basically a statistical technique that allows us to measure the relative value of each feature of a

product, with which it can be determined which combination of these features maximizes the probability

of being chosen by the consumer. Its application to marketing was conceived in 1974 by Paul Green,

professor in The Warton School.

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and Srinivasan (1974), Park and Srinivasan (1994) or Jourdan (2001). More

recently the work of Chung and Rao (2012), presents a general consumer

preference model that overcomes the limitations of consumer choice models,

when it is not easy to consider some qualitative attributes of a product or when

there are too many attributes. These works neither make up an overall and

quantitative measure of the brand nor show the value generation that this

measure has for the company. This must be determined in a business

combination. Nevertheless the finality is to understand the consumer's reactions

to carry out a proper brand positioning strategy in a market or to choose the

features a product must have to be launched with guarantees of success.

However, of interest for the implementation in our work are aspects such as

segmentation and analysis of heterogeneity in preferences and in consumer

loyalty for the good fit of the models and their predictive ability.

Although authors like Nomen (2005) or Torres and Coronas (2002) also

warn about the flaws of the royalty methodology and the necessity of including

statistical techniques to develop more objective models, the fact is that there is

still no generally accepted proposal.

A generally accepted financial model to determine the implicit royalty

should contain, first, explanatory variables that meet the objective and are

comparable and uniform for all companies in the sample measures. Second, it

should include an empirical test of how the variables that are used explain the

royalty paid. This, in turn, would have to check whether it is possible to segment

the market to identify different patterns of behavior in the generation of royalty,

and then, determine how is the behavior of the variables in each segment, as

well as their classification capacity to assign new implicit royalties.

3. THE SAMPLE OF COMPANIES, HYPOTHESIS, THEORETICAL

FRAMEWORK AND BASIS OF THE PROPOSED MODEL.

With the aim of producing convergence between the academic

environment and the professional environment, a process is proposed that

incorporates enough statistical tools in order to obtain higher accuracy and

objectivity in the brand valuation. The data of franchising, which are public and

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provided by the firms through specialized web pages, have been chosen for the

following industries: food, health and beauty and fashion (they are listed in

annex 1). Besides, the required royalties have been obtained for the operation

of franchising, as well as the number of establishment that each one of the

franchises has. In addition, economic and financial variables used in the

analysis have been obtained from SABI (system analysis of Iberian balances)

data base.

By this way, the study has been based on a statistical contrast on the

royalties paid in Spanish franchises belonging to different industries. It has

selected three sectors heterogeneous enough to each other to test whether the

model works the same way in each of them, and that are the largest in number

of companies and data among Spanish franchisors. With this study, the

following hypotheses are analyzed:

I. If the cluster technique can help to discover distinct groups in the

enterprises’ database. Also it is a tool to observe different economic

characteristics in the distribution of data.

II. At the beginning of the article several studies have been discussed

showing the inability of accounting variables to explain the values of companies

and especially their intangible values. For this reason, it is necessary to check if

the main economic and financial variables of Spanish franchisors have sufficient

explanatory power to explain the different groups of royalties collected and,

therefore, their brands.

III. If the linear discriminating analysis turns out to be a useful and

operative tool to properly explain the paid royalties and if the regressions and

the weights of the variables are the same or change for different industries.

IV. If the methodology is useful for predicting what would be the

corresponding royalty to a new company of the industry.

V. On the basis of the scores generated by the model, it can also be

considered if we can determine the strength of a brand compared to the other

competitors on the market regardless of the group it belongs.

In this paper, firstly, a Cluster analysis and, secondly, a Linear

Discriminant analysis (LDA) have been used, both through an SPSS statistical

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programme. From the first technique a K-Means Clustering analysis was made

in order to obtain a group classification for each industry, according to clearly

defined features that exogenous variables show. The best classification found is

the one corresponding to three groups, called: high, medium and low royalty. In

this way, every one of these records the elements of the sample that, on the

whole, achieve the maximum distance of its means, also known as centroids.

The exogenous variables, which have been selected, are a good

representation of the value drivers of a company from different points of view,

so that any analyst would take them into account in an assessment process.

They are the following:

1- Royalty or Income Fee that the lessor collects for the use of the

Brand in accordance with the franchise arrangement.

2- Total Establishment, including these that are operated by own firm

or by third-parties.

3- Market Share, measured from the result of dividing the turnover of

each franchise between the total incomes that each industry

generates.

4- Gross Operating Margin, i,e. the quotient obtained dividing gross

profit (which is at the same time the difference of the sales and the

cost of the same) and the turnover obtained for each company.

5- EBITDA Margin, i,e. the quotient between the earnings before

interest, taxes, depreciation and amortization (EBITDA) and the figure

regarding revenues.

6- Return on Assets or ROA, known as the quotient between EBITDA

and the net book value of the asset.

7- Employees Productivity (EBIT/Employee), measured as the ratio

among the earnings before interest and taxes (EBIT) and the number

of employees.

The groups resulting from applying the technique of cluster basically

match those that come from manually classified companies only based on their

royalty, without the rest of the variables, which shows that the market is efficient

and there is a clear relationship between groups of prices paid and economic

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characteristics of the enterprises. Table 1 introduces classified differences

which have been obtained from the proposed variables for each industry.

Table 1. Classification from K-means cluster.


We can highlight (see also figure 4) the presence of remarkable

differences over all magnitudes that have been used, among the cluster of each

industry. Specifically, the average royalty collected by the food industry is 5.5%,

larger in comparison with the health and beauty, and fashion industries, which

are 4.56% and 2.56%, respectively. On the other hand, high variability among

the means of the groups can be seen (the group of high royalty generates

exogenous variable values greater than the medium and low range). In the case

of the food industry, we find that the exogenous variable that represents the

number of Total Establishments is up to 159 for aggregated companies in the

high royalty, while for the second group, medium royalty, it is just 18 aggregated

shops and for the low royalty, just 17. This also happens for the other variables:

Market Share is up to 4.08% in the first and, in the second, it falls by up to

0.27%, while for the third it is even lower, just 0.01%. Looking at the Gross

Margin, it is 49.64%, falling to 33.25% and reaching a negative figure of -

11.51% for the low royalty cluster; EBITDA margin represents 13.95%,

decreasing to 5.20% and again turning into a negative value of -79.09% in the

FoodHealth and Beauty Fashion Mean Stand.Deviation Mean Stand.Deviation Mean Stand.Deviation

Tot.Establishments 17.91 14.25 19.21 18.11 15.92 11.24

Market Share% 0.27 0.66 0.78 1.98 0.92 1.37

Gross Margin % 33.25 16.07 47.52 20.83 32.55 15.79

EBITDA Margin % 5.20 13.64 10.10 11.67 6.19 9.42

ROA % 0.07 12.99 7.17 10.31 3.90 11.48

EBIT/Employee 4.50 27.61 8.84 21.00 5.07 12.05

Tot.Establishments 15.91 23.59 24.00 25.20 80.50 48.05

Market Share% 0.01 0.02 1.17 3.26 3.56 5.68

Gross Margin % -11.51 39.62 21.46 20.17 31.30 9.24

EBITDA Margin % -76.09 47.34 -13.57 15.65 3.05 15.91

ROA % -54.54 29.18 -19.15 22.76 1.77 12.37

EBIT/Employee -33.43 36.78 -10.12 9.83 6.78 16.29Tot.Establishments 158.74 122.00 199.00 145.86 75.50 105.36Market Share% 4.08 6.20 3.74 7.34 0.96 1.29Gross Margin % 49.64 20.54 51.09 22.87 80.93 81.87EBITDA Margin % 13.95 16.21 13.97 13.70 31.97 11.08ROA % 4.92 7.82 16.16 19.81 21.96 27.92EBIT/Employee 81.14 323.50 39.99 46.59 327.67 350.37Tot.Establishments 54.26 88.38 47.49 85.75 38.40 43.97Market Share% 1.22 3.57 1.35 3.72 1.75 3.54Gross Margin % 30.77 29.07 39.60 24.24 33.85 19.71EBITDA Margin % -4.77 37.61 3.00 17.58 6.10 12.77ROA % -6.90 25.39 -0.03 21.44 3.86 12.58EBIT/Employee 18.73 168.40 7.32 28.62 16.93 77.08

Total 73. Royalty 5,50



Low Royalty 4.2. Nº of cases 11.

Low Royalty 3.1. Nº of cases24.

High Royalty 6.5. Nº of cases 19.

High Royalty 6. Nº of cases 11.

FOOD

Medium Royalty 5.4. Nº of cases 43.

Medium Royalty 5.2. Nº of cases 39.

FASHION

Low Royalty 2. Nº of cases 18.

High Royalty 4. Nº of cases 2.

HEALTH AND BEAUTY

Medium Royalty 3. Nº of cases 37.

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low royalty cluster. The Return of the Assets (ROA) is 4.92%, descending to

0.07% and -54.54% respectively, and finally, EBIT per Employee is 81.14 (in

thousands of euros) for the high royalty group, while for these companies that

have been classified as medium royalty, it is notable that its figure falls by up to

4.5 (in thousands of euros) per worker, and again, the value for the low royalty

is below zero.

Figure 4. Differences between cluster figures in food sector.


Once the different groups for each industry have been obtained, the LDA

is utilized in order to check if each one of the variables used is an adequate

indicator to explain the royalty paid. Following Dillon et al (1978): “if the

assumptions of normality and identical covariance matrices are satisfied, them

there is no reason to examine alternatives to Fisher’s LDF (discriminant

functions)4”. In this way LDA assesses can be employed to determine what the

implicit royalty for a company will be. For that purpose, a previous descriptive

univariate analysis has been used, through the Wilks’ Lamba, �, which is equal

to the quotient between the sum of the square of each group (squared

deviations of each data point concerning the mean of the groups or centroid) 4 In many marketing analysis explanatory variables are usually qualitative, in the sense that each variable

takes on a small number of values and the Fisher’s LDF method can introduce distortions. However, in

our analysis the explanatory variables are quantitative, tend to fulfill a normal distribution and equal

covariance matrices according to The Box's M test.

-100,00

-50,00

0,00

50,00

100,00

150,00

200,00

high Royalty

Medium Royalty

Low Royalty

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and the total square sum (squared deviations of each data point concerning the

mean of the whole points). The closer the result is to zero the greater will be the

discriminating power and this is a consequence of the differences between the

groups but not within them.

Table 2. Univariant Wilks’ Lambda.


As can be perceived in table 2, practically all Lambda values are

significant for the classification of the groups, that is, they show a p-value lower

than 0.05, except for the EBIT/Employee ratio in the case of the food industry.

Actually, the Market Share variable is not significant in the case of the health

and beauty industry, and the same occurs with ROA in the fashion industry.

Nevertheless, despite the fact that these variables are not significant individually

it does not prevent the proposed model being significant. The linear

discriminating analysis can be considered as a regression technique that

establishes linear relationships, where the endogenous variable is a categorical

one (determines the groups) and the exogenous variables, being continuous,

will help to predict the belonging group. To achieve this mathematically, series

of equations are going to be generated (two in this case as we have three

classification groups) with the aim of discriminating or separating all the groups

as much as possible:

εα +++++++= odbROAbEBITDAbGrossMbMSharebEstbD jjjjjjj Pr***arg*** 654321

(1); j=1, 2;

Wilks' Lambda Sig.

Wilks' Lambda Sig.

Wilks' Lambda Sig.

Tot.Establishments .501 .000 .447 .000 .507 .000

Market Share% .771 .000 .924 .061 .878 .030

Gross Margin % .561 .000 .725 .000 .788 .002

EBITDA Margin % .343 .000 .562 .000 .835 .008

ROA % .360 .000 .592 .000 .917 .097

EBIT/Employee .945 .137 .680 .000 .398 .000

FOOD FASHIONBEAUTY AND HEALTH

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For Est= Total establishments; MShare= Market Share; GrossMarg= Gross

Margin; EBITDA= EBITDA Margin; ROA= Return of Assets; Prod= Employees

Productivity.

These linear combinations of the six variables used must maximize the

variance between the groups and, at the same time, should minimize it within

groups.

�� = �� = �� + �� (2);

In equation 2, �, we have the vector of coefficients; T, the matrix of total

variances and covariance; E, the matrix of variances and covariance among the

groups and D, the covariance within groups. The vector of coefficients � must

maximize the relationship of the variance among the groups regarding the

variance within the groups. This quotient is also known as eigenvalue,

described in (3):

�� = �� ! �3�;

To evaluate the importance that each variable possesses specifically, in

equation (1) we have employed the forward stepwise method in SPSS (table 3),

which consists of elaborating sequential models in which an additional variable

is included time each time until they stop providing significance and so, once

this point is reached, the procedure ends. It should be mentioned that the

predictors included are continuously reevaluated, so that if a variable has not

enough discriminating power, or is explained by others (meaning that it lacks

any specific contribution of its own) it is deleted. Subsequently, these variables

that have been included or excluded from the model must be shown for each

segment. Forward stepwise method will choose firstly the variable that has the

best abilities for explaining the group differences; afterwards, through the step

by which the variable has been selected we can ascertain its importance. This

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is made by using the global Wilks’ Lambda5 of the model and the F statistic6

after adding the said variable. Throughout the sequential steps there is

verification of how the Lambda goes down, which means that the discriminating

power is improved among the groups.

Table 3. Results of the variables selection to the stepwise method.

Resource: own elaboration.

In the table above it is easily observable that included-excluded variables

change for each industry. Market Share is dropped from all the sectors.

However, in the case of the food industry the Gross Margin is also eliminated.

For the health and beauty industry two additional variables do not provide any

discriminating value: they are the ROA and EBIT/Employee. It is not a

coincidence on a transversal level of the industries to observe the degree of

importance of the proposed variables in equation (1). In the case of the food

industry, EBITDA Margin is the variable that provides the highest discriminating

value, while for the health and beauty industry the same one is relegated to

second place, below the Total of Establishments and for the fashion industry it

decreases in importance down to fourth place, after EBIT/Employee, Total

Establishments and Gross Margin variables.

5 Wilks’ Lambda, λ, from table 2, as a difference from individual of table 1, distinguishes the group

created after the inclusion of the new variable of each step. 6 This criteria works in the following way, if the value of F is higher than 3.84 (the critical 0,05

significance), the variable will be included, if not, it will be deleted. However, finally a minimum output

value which must be at least 2.71 is required. The F statistic as a function of Wilks λ is measured by the

following formula: $%&'&('&) * +)&,-.//,-

,-.//,- 1 where n is the number of elements of the sample, g the number of

groups and p the number of exogenous variables. The result gathers the change in the value of λ after the

incorporation of every variable in the model. Forward stepwise procedure can introduce those variables

which fulfill the requirements of the F statistic and also have the lowest value of the Wilks Lambda

statistic. However, if finally it is a minimum level of tolerance, which is defined as 1-r2, r

2 being the

determination coefficient of the multiple linear regression, in which the dependent variable is the one that

has entered in the final stage and the explanatory ones are those that have entered in previous steps. In

this paper the level of tolerance by default using SPSS is 0.001.

Steps Fratio Wilks´Lambda Steps F ratio Wilks´Lambda Steps F ratio Wilks´Lambda

Tot.Establishments 2 34.24 0.18 1 39.31 0.45 2 30.27 0.16Market Share% Elim Elim Elim Elim Elim Elim Elim Elim ElimGross Margin % Elim Elim Elim 3 3.85 0.24 3 36.52 0.09EBITDA Margin % 1 13.44 0.34 2 13.72 0.26 4 16.72 0.08ROA % 3 9.14 0.14 Elim Elim Elim 5 10.52 0.05EBIT/Employee 4 4.67 0.12 Elim Elim Elim 1 121.85 0.40

FOOD BEAUTY AND HEALTH FASHION

18

4. APPLICATION AND RESULTS OF THE PROPOSED MODEL.

In this research we will use as discriminating scores those obtained

from canonical equations and, in second place, through the Fisher

discriminating functions. In the first case, this kind of equation shows us the

influence that each exogenous variable has in the variability of one group over

another, in addition to which groups keep a larger differentiation between them.

Table 4 presents the result of two canonical functions. Each one of them

explains the differences in the behavior of each pair of groups. The first one will

always have a larger eigenvalue (see equation (3)), canonical correlation, or

which is the same7, the proportion of the variation explained by the differences

between the groups and the total variation, which is very high in all cases.

Table 4. Canonical functions.


From the previous analysis we also obtain these coefficients that allow

us to appreciate the weight that introduced variables have in the analysis as

well as the consequences that each one of them has for the result of the

canonical equation. Nevertheless, to make a good interpretation of the

coefficient sign and the relationship between the three groups of royalty it is

necessary to know the value of the centroids by using the following equation:

εα +++++++= jodjbjROAjbj

EBITDAjbjGrossMjbjMsharejbjEstj

bjD Pr*6*5*4arg*3*2*1 (4)

In the LDA each canonical function explains the relationship between

the centroids whose distances are maximized. These values are given in table

7 A more precise definition is the square root of the sum of the square between the groups and the total

sum of the square expressed as a quotient 2�332 = 456755685

Eigenvalue% of

varianceCanonical correlation Eigenvalue

% of variance

Canonical correlation Eigenvalue

% of variance

Canonical correlation

1 3.06 75.01 0.87 1.42 65.39 0.77 7.41 86.06 0.94

2 1.02 24.99 0.71 0.75 34.61 0.65 1.20 13.94 0.74

Function

Food Health and beauty Fashion

19

5. With this method, as in our analysis we have created three groups, two

canonical equations have been made. The first function (D1) explains the

differences between the high versus low royalty for food, health and beauty,

while the second function, (D2) shows the relationship between the medium and

low royalty for food, and it compares the score of a medium value relative to a

low one for health and beauty. For its part, fashion, (D1) accounts for the

behavior of the high versus medium royalty, while (D2) shows the behavior of

the medium versus low royalty.

Table 5. Centroids by canonical functions.


It is observable that in the food industry, the centroid of the high royalty

group is located in D1 at the positive extreme, whereas the mean of enterprises

with medium royalty is in the center of the interval and finally the sign of a low

royalty is negative. In this way we can interpret the rest of values in table 5. The

standardized coefficients of canonical discriminating functions are given in table

6.

Table 6. Standardized coefficients of the canonical functions.


The function that best separates groups in the three industries analyzed

is D1. Thus, the food industry shows positive in the following coefficients: Total

Establishments, EBITDA Margin and ROA, and thus, when each one of these

variables increases, it raises the probability of the enterprises belonging to the

D1 D2 D1 D2 D1 D2

Medium 0.40 -0.79 -0.09 0.80 -0.72 -0.73High 1.38 1.47 2.60 -0.75 13.80 -0.65Low -3.95 0.57 -1.05 -0.96 -0.05 1.57

FashionFood Health and Beauty

D1 D2 D1 D2 D1 D2

Tot.Establishments 0.27 0.98 0.82 -0.59 0.23 1.19

Market Share% -- -- -- -- -- --

Gross Margin % -- -- 0.28 0.37 1.31 -0.69

EBITDA Margin % 0.67 -0.27 0.38 0.71 -1.56 0.82

ROA % 0.55 -0.15 -- -- 1.06 -0.39EBIT/Employee -0.14 0.52 -- -- 1.90 -0.22

FOOD HEALTH AND BEAUTY FASHION

20

high royalty group, On the other hand, EBITDA Margin is the variable in D1 that

best explains the differences between the groups because it is the one that has

the highest coefficients in absolute terms.

In this research, a confusion matrix has been used in order to determine

the descriptive model´s goodness of fit. This matrix compares the real

classification, which was elaborated using cluster technique, and the one

obtained by the linear discriminating function, using this way to check the

number of goals that were achieved in each one of the analyzed industries. As

is observable in table 7 this number is very high, because it is up to 96.5% for

the case of the food industry, 89.2% for the health and beauty industry and,

finally 96.5% for the fashion industry. The mechanism of classification for each

individual location depends on the odds of Bayes Theorem8.

Table 7. Confusion matrix

Source: own elaboration. 8 The probability of belonging to a group given a specific discriminating punctuation 9�:;/<=)

=>?@A/BCD∗F�GH �

∑ >?@A/BCD∗F�GH �BAJ/; p�gM � is the prior probability of each group and 9�<=/'C� is the probability that a

punctuation di belongs to a group gk.

Medium 5 Low 4 High 6Original Count Medium 43 0 0 43

Low 1 10 0 11

High 2 0 17 19

Medium 100.0 0.0 0.0 100.0

Low 9.1 90.9 0.0 100.0

High 10.5 0.0 89.5 100.0

beauty

Medium 5 High 6 Low 3

Medium 36 0 3 39

High 2 8 1 11Low 2 0 22 24Medium 92.3 0.0 7.7 100.0High 18.2 72.7 9.1 100.0Low 8.3 0.0 91.7 100.0

FashionLow 2 High 4 Medium 3

Low 16 0 2 18High 0 2 0 2Medium 0 0 37 37Low 88.9 0.0 11.1 100.0High 0.0 100.0 0.0 100.0Medium 0.0 0.0 100.0 100.0

Total

Total

Food

Predicted group membership

Total

Original Count

Classifies well 96.5% of

the cases

Classifies well 95.9 of the cases


Original Count

Classifies well 89.2% of

the cases


21

Another way of measuring allocation scores for each group is through

Fisher equations. Both methods lead to the same classification. However using

a Fisher equation helps to determine the brand strength, which was described

in detail in the second section of this paper and was widely used in all the

models for brand valuation. To provide an example, the Fisher equations for the

food industry are in table 8:

Table 8. Coefficients of Fisher linear discriminating functions.


Three equations are measured: F1 which represents the medium royalty,

F2 for the low royalty and F3 explains the higher royalty.

ε++−++−= odROAEBITDAEstF Pr*00,0*005,0*012,0*005,017,11 (6)

ε++−−+−= odROAEBITDAEstF Pr*008,0*174,0*134,0*007,0851,102 (7)

ε+++++−= odROAEBITDAEstF Pr*006,0*007,0*014,0*044,0935,43 (8)

From the above equations, a Pr (g/F) probability can be calculated, in

other words, if a company obtains a discriminating score F, then it will belong to

g group. From (Uriel 1996) the following formula (the result of which coincides

with Bayes rule considering the priori probabilities of groups as equal), may be

applied:

Pr�:/O� = PQBPQ/RPQSRPQT (9)

Therefore, if a company i has, for example, 201 Establishments, an

EBIT/Employee equal to 17.04 (in thousands of monetary units), ROA equal to

14.03%, an EBITDA Margin of 18.48%, then F1=-0.24, F2= -9.38 and F3= 3.95.

Consequently, applying (9), Pr(1/F)=0.015, Pr(2/F)=0, Pr(3/F)=0.985. Then it

Medium Royalty 5 Low Royalty 4 HIgh Royalty 6 Total Establishments .005 .007 .044

ROA -.005 -.174 .007

EBITDA Margin % .012 -.134 .014

EBIT/Employee .000 .008 .006

(Constant) -1.172 -10.851 -4.935

22

could be predicted that this company belongs to the third group, or high royalty,

because this is most likely, obtaining at the same time a score F3= 3.95, the

larger of the three expressed above. In this way, a shortlist of three values Pij=

(Pi1, Pi2, Pi3) would be assigned for each company, Pij being the probability of a

company i belonging to the group j.

U = �O=V/9=V = W��VX),Z,[,{9=V}� (10)

Thus X is the Fisher score vector which is the result of the probability of

each company pertaining to the most likely cluster. In order to determine the

companies’ brand strength, once the previous vector had been measured, it

must be divided into deciles. The fifth and tenth decile corresponds to the low

royalty cluster. The twentieth decile contains two elements which have obtained

the highest score in the low royalty group and the rest are those that have

obtained the lowest scores in the medium royalty cluster. For its part, the

thirtieth, fiftieth and seventieth deciles include the remaining companies of the

medium cluster with successively larger scores and finally, the eightieth,

ninetieth and ninety-fifth deciles have been set up using these companies that

have been classified as high royalty. Each decile, O��, represents the

cumulative probability of these scores. So O��, or Brand strength will collect,

through a score from 0 to 100, the level or strength with which the attributes or

independent variables of any company in the sector adds value with respect to

the whole sample, as it is shown in table 9.

Table 9. Brand strength expressed as a function of deciles.


5 10 20 30 50 70 80 90 95-11.05 -10.74 -3.46 -1.15 -1.11 -0.98 0.11 1.94 10.43

-10.83 -10.70 -1.15 -1.14 -1.07 -0.78 1.18 3.05 18.23

-11.62 -10.82 -10.54 -1.15 -1.15 -1.07 -0.56 1.32 3.95

4.07 4.23 4.98 5.18 5.20 5.26 5.81 6.72 10.59

4.18 4.49 5.17 5.18 5.22 5.36 6.34 7.28 14.12

3.79 4.19 4.33 5.17 5.18 5.22 5.47 6.41 7.73

Deciles for 73 elements. Food Industry

Average Xij decile

Average royalty decile

Maximum royalty decile

Minimum royalty decile

Maximum Xij decile

Minimum Xij decile

2.97

6.50

Fisher average cluster -10.79 -1.07

Royalty average cluster 4.20 5.21

23

In our example, in addition to putting the new company i within the high

royalty cluster, as was indicated before, the strength of its brand against other

competitors would be between 90 and 95 deciles because its score was 3.91,

closer to 95 than to 90. Finally, based on the strengths the royalty rate can be

determined that would correspond to charge. It would be between the maximum

decile 90, 7.28% and minimum decile 95, 7.73%. Once the range of implicit

royalty is enclosed the value of the brand may be determined, as is described in

section 2.

Figure 5. Percentiles and Brand strength.


The results shown in table 9 and in figure 5 are very useful because

they allow us to establish a hierarchy between companies according to the

value of each discriminating score and the corresponding cumulative probability

or brand strength. Thus it is possible to compare different companies in the

same sector regardless of the group or cluster to which they belong, and thus

their overall position is determined in the market against other competitors.

0,00

2,00

4,00

6,00

8,00

10,00

12,00

14,00

16,00

5 10 20 30 50 70 80 90 95

Ro

ya

ltie

s %

Percentiles

BRAND STRENGTH. FOOD INDUSTRY

Medium Royalty Minimum Royalty Maximun Royalty

24

5. CONCLUSIONS AND FINAL REMARKS.

From the described statistical methods: cluster technique and LDA, we

have developed a model to determine the implicit royalty for buying or using a

brand or a corporate image. On the one hand, this study is vital for companies

which want to expand through franchises, but, on the other hand, it also allows

us to determine the flow corresponding to a certain brand in order to ascertain

its value. However, in section 2 the complete methodology is indicated to value

a brand, once that the royalty has been determined. This process is not

developed in this article and it will be part of future works and lines of research.

Other statistical techniques, such as discriminant and multinomial logit

models, have been widely analyzed in marketing studies, as in Perreault et al

(1979), Kamakura and Russell (1993) or Swait et al (1993), but only from the

point of view of the analysis that certain features confer to the product or to the

buyers in order to increase its usefulness for the consumer and, accordingly, to

achieve the best position in the market. The discriminant analysis has also been

used from the middle of the last century to solve other financial problems, such

as the prediction of bankruptcies, as in Altman (1968). However, the suitability

of the LDA for this purpose has never been proved, despite authors like Nomen

(2005) or Torres and Coronas (2002) warn of the flaws of the royalty

methodology and the necessity of including statistical techniques.

After being implemented, the model turns out to be efficient in all five

proposed aspects. Firstly, cluster technique proves to be useful for segmenting

the companies according to the royalties paid and the other financial variables

used (see table 1 and figure 4). Secondly, the used exogenous financial and

accounting variables generate enough discriminating power to explain the

different groups of royalties which are collected in a certain industry (see table

3). Although authors like Lev (1989, 2000, 2005), Brown et al (1999), Dantoh et

al (2004) Iñiguez and López (2006) or Rubio et al (2013), among others,

demonstrate the ineffectiveness of accounting variables to explain the value of

intangibles developed by companies, is not for the specific case of the brand.

This is not surprising since other qualitative variables (leadership, stability,

support or image, among other) when creating a value must be expressed in

terms of improvements of the balance's variables and of the companies' income

25

statement, such as: market share, sales, margins and, ultimately, an increased

structural profitability. Thirdly, the canonical equations have proved to be a very

useful tool to explain how exogenous variables generate value for the creation

of brands, although the procedure and their importance are different depending

on the industry, as shown in tables 5 and 6. Fourthly, this technique suitably

predicts the group to which a new company belongs for all the addressed

industries (see table 7) and, lastly, using the scores obtained with the equations

of Fisher, a ranking of the companies can be created to determine the brand

strengths, which is confirmed by their market positioning with respect to other

competitors, as set out in table 9 and figure 5.

It is important to emphasize that the quantification and valuation of the

intangible assets owned by the companies are still a challenge for economic

science. Thus, despite the existence of several valuation models, they are

usually immersed in great doses of subjectivity or their developments even lack

the proper mathematical and statistical techniques. This fact does not allow the

existence of an agreement for its general acceptance. However, on the one

hand, the proposed model works with quantitative variables which were made

public through the annual accounts filed at the Mercantile Registry. This

involves using as a base for the calculation standardized criteria that are

generally accepted in comparison with other possible qualitative variables

whose elaboration changes according to how or by whom it was made.

Furthermore, the royalties paid in the Spanish franchise market were taken, as

advised by financial accounting standards.

The proposed model could be used in the future to predict brand value

for other companies and industries if it has databases similar to those

generated in this work. Another application might be to check the efficiency of

the proposed model with other qualitative and quantitative data, analyzed

correlations between variables, across different markets and products, and

check which provide more information to generate the royalty paid.

It is also a comparable market method. Consequently, the proposed

technique becomes an optimal way to determine the value that an independent

buyer would be willing to pay for using a brand, from another with similar

features. The analysis discussed here can be considered as a market technique

and therefore it also serves as a complementary method for other models which

26

try to find an internal perspective of the generation of value of the intangibles

within the company, like the method of excess of benefits in multiple periods or

even the one concerning real options. The use of several complementary

models in a valuation process can ensure that there are no biases or

subjectivities and also that it includes the different points of view that any

assessment process should emphasize, especially the valuation of an intangible

asset that is as representative in the modern economy as the brand.

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30

ANNEX 1.1. FRANCHISING COMPANIES. FOOD INDUSTRY. AKRA FRÍO, S.L.

ILOPEZHIDALGOA

ALADA 1850, S.L. INVERSIONES VENESPOR, S.A.

ALMA DE CACAO INVESANVEL, SL

ARROCERÍAS DE ALICANTE, S.L.U. JUNIO 1972 RESTAURACIÓN, S.L.

ARROCERÍAS DE ALICANTE, S.L.U. KANIKAMA PROJECT, SL

AZEITE E VINAGRE, LDA. KRUNCH FRANCHISING, S.L.

BAÑOS GARRE, S.L. L.N.F. FRANCHISING S.L.U.

BEER & FOOD GRUPO DE RESTAURACIÓN LA BOHEME

BEIRUT KING S.L. LA CUEVA 1900

BIERWINKEL, S.L. LA SUREÑA

BODEGAS GALIANA ALIMENTACIÓN, S.L. LACREM, S.A.

BRASERÍA LOS DUENDES S.L. LLOGUECATA, S.L.

BRUSTERS RESTAURANTES 2010, SL LOS BODEGONES

BURGER KING ESPAÑA, S.L.U MAC PAPAS, S.L.

BURGER RICKY MATERASTURIAS, S.L.

CAFÉ DEL MERCADO FRANQUICIAS, S.L. MIGUEL SANCHO S.L

CAFÉS CANDELAS SL MY CREPE, SL

CENTRAL DE FRANQUICIAS PANTAIBERIC O REI DAS TARTAS

CHOCOLATES VALOR, S.A. ODRE Y HOGAZA, S.L.

CIA. DALLAS RIB´S, S.A. OPEN 25

COFFEE & FOODS OVERPANI FRANQUICIAS, S.L

COMESS GROUP DE RESTAURACIÓN PANA-ROM S.L

COMESS GROUP DE RESTAURACIÓN, S.L. PANSFOOD, S.A. (THE EAT OUT GROUP, S. L.)

COMESS GROUP DE RESTAURACIÓN, S.L. PARSIN´S, S.L

COMESS GROUP DE RESTAURACIÓN, S.L. PASTIFICIO SERVICE, S.L.

COREN GRILL, S.A PECADITOS

DEHESA SANTA MARIA FRANQUICIAS S.L. PIZZA LEGGERA WORLDWIDE

DIABLITO FRANCHISING, S.L. PIZZA MARZANO, S.A.

DISTRIB. ALIMENT. SURESTE SAU POLLO CAPORAL FRANQUICIAS, S.L.

DON ULPIANO FRANQUICIAS S.L RAMÓN Y VIDAL, S.L.

DOOPIES & COFFEE, SL RECREATIVOS TORNAJUELO

DRUNKEN DUCK FOOD, S.L. RESENDE, S.L.

DUNKIN ESPAÑOLA S.A RESTAURANTES BRUNO, S.L

EL MOLÍ VELL RIGARUSSO ASOCIADOS, S.L.

EURO TAPASBAR, S.A. RODILLA SÁNCHEZ, S.L.

EXPANSIÓN DE FRANQUICIAS, S.L. SAFO MEDITERRÁNEA, S.A.

EXPANSIÓN DE FRANQUICIAS, S.L. SAMORVARTE Y CAFÉ, S.L.

FABORIT COFFEE SHOP, S.L. SDAR SRL

FOOD SERVICE PROJECT, S.L. SERVIFRUIT GOMAB S.L.

FRANCHISINGS KURZ & GUT, S.L SPORTS FRANCHISING, S.L.

FRANQUICIAS LA PIEMONTESA SLU STICKHOYSE BCN, SL

FRANQUIPAN, S.L. TELEPIZZA, S.A

FRIENDS & MOJITOS S.L TERRA VITAE, SL

GINOS FRANCHISING, S.L. THE EAT OUT GROUP, S.L. (BOCATTA 2000, S.L)

GRAN PALADAR SL UNIDE

GREEN DEVELOPPEMENT URBAN LIFE FOOD COURT

GRUPO ALASKA VINUS BRINDIS S.L.

GRUPO RESTALIA YUM RESTAURANTS ESPAÑA

GRUPO ZENA DE RESTAURACIÓN, S.A. ZUMO BAR CANARIAS S.L.

GRUPO ZENA DE RESTAURANTES S.A.

HOSTELOESTE, S.L.U.

31

ANNEX 1.2. HEALTH AND BEAUTY INDUSTRY.

ABANOLIA INSTITUTO SUPERIOR DE ESTUDIOS PSICOLÓGICOS (ISEP)

ACORDE PLUS, S.L JEAN LOUIS DAVID FRANCHISE SPAIN, SL

ACR WAX COSMETICS, S.L. JUAN ALBERTO RECIO S.A.

ACR WAXCOSMETICS, S.L. KAROON PILATES S.L.

ACTUAL STHETIC, SL LABORATORIO LUCAS NICOLÁS, S.L

ALDABE S.A. LASER NATURA

ALDABE, S.A. LEBRON FRANQUICIAS

ALDABE, S.A. LIPOTHERM CENTER

ALTA ESTÉTICA SL LOGIC MEN

ASOCIADOS LLONGUERAS MÉTODOS MASSANA, S.L.

BE STHETIC METRODERMO, S.L.

BEN&SULY S.L MH FITNESS FEMENINO

BENESSERE FITNESS PILATES NUTRICIÓN. MONDARIZ SPA S.L.

BENZAQUEN2,S.L MÜLLER MARKETING GMBH.

BODY FACTORY FRANQUICIAS, S.L. MULTIFRAN SPAIN S.L.

BYE BYE PELOS, SL NEGREVERNIS S.L.

C&C CASANOVA S.A. NEOGYM CENTER VALENCIA SLU

CEBADO, S.A NI UNO +

CEFOGA 2000, S.L. NO MÁS DIETAS SL

CELLULEM BLOCK NOMASVELLO, SL

CENTROS DESESTRES, S.A NOW YOU ENTRENAMIENTO

CENTROS PULSAZIONE INTERNACIONAL, SL NUEVAS LATITUDES S.L,

CLÍNICA SACHER – MEDICINA ESTÉTICA. OH MY CUT!

CLÍNICAS CAREDENT, S.L. PELLBELLADEPILAT

CLÍNICAS CETA S.L. R-DIFUSIÓN, S.L.

CLINICAS PODOLOGICAS RUHAM CONCEPT 2011, SL

CLÍNICAS VIRGEN DE LA PAZ S.L SALUD CORPORAL S.L.

COMPAÑÍA DE SERVICIOS MÉDICOS AMENTA S.L. SANTS INSTITUT GROUP

CONTIFARMA, S.L. SENTRALGES S.L

CONTOURS EXPRESS IBÉRICA S.L SERVICIOS DE DEPILACIÓN BLOC, S.L

COVALDROPER GRUPO SEVEN SECREETS

CUERPOS FITT SISOL SYSTEM, S.A.

CURVES INTERNATIONAL OF SPAIN, S.A. SISTEMA FORMADOR CONSTRUCTORES DE EXITO SL

D ELITE EVENT PLANNERS SL SLIM BALANCE, S.L.

DENTALIS SOC. DE GESTIÓN DE SIEMPRESOL S.L.

DEPICOOL SOLIVISE PROTOCOLOS DE ESTÉTICA AVANZADA, S.L.

DEPILINE WAX & COSMETICS S.L. SOLMANÍA S.L.

DORSIA CENTRAL DE COMPRAS, SL SPA ZENCER ESTHETIC & CULTURE, SL

ECOLOGIC BY LINDA NICOLAU, S.L. SPEJOS DIFUSIÓN, S.L.

ELIMINA EL VELLO TENDENCIES LONDON, S.L.

EPILAE NORTE, S.L. TERSUS CUIDADO Y BELLEZA

ESTETICA Y SALUD MASCULINA S.L. THERAPYFISH-SPA, C.B.

ESTETICBODY TORNEMHI INTEGRAL, S.L

ETHIA CENTROS MÉDICO ESTÉTICOS UÑAS 10

EXTENSIONMANIA UP NAILS, SL

FITNESS19 VILLACASTÍN MED, S.L.

FK ESTETICA INFANTIL S.L. VIVAFIT

FRANCK PROVOST WOMAN 30, SL

GIRÓN & NAVARRO INTERNACIONAL WSI RETIRO, S.L.

GLOMONT S.L YNSADIET, S.A.

GRUPO ACTUAL ESTHETIC - BIOTHECARE ESTETIKA ZELOS COSMETICS S.L.

GRUPO EMPRESARIAL MEDITERRÁNEA DE MEDICINA Y ESTÉTICA SL

32

ANNEX. 1.3. FASHION INDUSTRY.

ARZANO, S.L. PILI CARRERA, S.A.

CALDERÓN FRANQUICIAS, S.L. PLATAX ORFEBRES, S.L.

CÁLLATE LA BOCA POETE S.L.

CHAQUE S.A. RECSTORE

CHICCO ESPAÑOLA, S.A. SELCONET, S.A.

CITY FINANCE 2011, SL STAR TEXTIL, SA

CRYSANNA COLLECTION, S.L SUPERDRY

CUPPERTON DEVELOPS S.L. SUX TRIT, S.L.

DISTRIBUCIONES INTERNACIONALES M.ERCILLA, S.L TEXTIL TEXTURA, S.L.

DIVINA PROVIDENCIA THIS WEEK

DM2 ESTILO MODA S.L. VARLION ESPAÑA S.L.

EIGHTEEN OCTOBER 2001, S.L. WOLFORD ESPAÑA, S.L.

EMERGENCIA PERMANENTE, S.L.

EMERGENCIA PERMANENTE, S.L.

EMPORIO FRANCHISING

EVA ALFARO

FERNANDEZ-MATAMOROS MAS-SARDA JOSÉ MARÍA S.L.N.E

FRANDESIM, S.L.

FRANQUICIA LAS LILAS S.L.

GLOBAL DE PRODUCTOS ONLINE S.L

GOLD SYSTEM MERCADO, SL

GROUPE ZANNIER ESPAÑA S.A

GRUPO LOVE STORE, S.A.

GRUPO OSBORNE S.A.

GRUPO ROSA CLARÁ

GUBESA CO, S.L

IKKS SPORTSWEAR SPAIN

INDUSTRIA FRANCO ESPAÑOLA DE MODA, S.A

INTERMALLA, S.L.

JONAS 3000,S.L

JORDI ANGUERA ESPAI I COSTURA SL

JULIO MAESTRE, S.L.

KARPI CONFECCIÓN, S.L.

KIABI ESPAÑA - KSCE, S.A.

LA COMPAGNIE DES PETITS

LA NÁUTICA SERVICIOS NÁUTICOS, SL

LA POUNTY S.L.

LA TIENDA DE LOLÍN FRANQUICIAS, S.L.

LAPEGAL ARTE LEÓN S.L. BULKA

LOURIDO Y REAL S.L.

LUXENTER SHOPS

MACSONSA

MARFA TESSILE, S.L.

MEIGALLO

MIROGLIO ESPAÑA, S.A.U.

NECK CHILD S.A

OPI PRENDAS INFANTILES ORCHESTRA S.L.

PASARELA CLM S.L.

PATRIC SPORT, S.L.

PATRICKS S.A

PIEL DE TORO

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